Robot manipulators intelligent motion and force control

Abstract

Typically for industrial robots, users have no access to motor torque control. Instead, a robotmay be controlled by an external position or velocity command interface. This outer-loop external interface facilitates sensor-guided and robotic shared motion control. However, the robot trajectory tracking performance is affected by the unknown robot dynamics and joint servo controllers. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. Thus, it is necessary to compensate for the internal dynamics to improve the motion control performance. However, it is in general a difficult problem to analytically identify a highly nonlinear and coupled robot dynamical model. Instead, a universal function approximator such as a neural network may be adopted for modeling the system dynamics.

Robot manipulators have been widely utilized in industrial automation to improve productquality and productivity. Common robotic tasks in an industrial setting include assembly, welding, surface treatment, packaging and labeling. The various tasks also stimulate the development of sophisticated control algorithms and dexterous tools for robot manipulators.

This thesis investigates the application of optimization and deep learning to robotics motionand force control. First, by leveraging a position control interface, a kinematic controller is developed by solving a quadratic-programming problem. However, the motion control performance is obviously compromised by the unknown internal dynamics when the robot is commanded a fast trajectory. To improve the motion control accuracy, we then develop a model-free gradient-based iterative learning control (ILC) algorithm with the gradient descent directions generated by the physical system. However, the algorithm may be time-consuming considering iterative updates, and the learned representation is not transferable to a new trajectory. This motivates the use of neural networks to approximate the system dynamics to improve the time efficiency and generalization of ILC. And a neural-learning control framework is proposed to address robot motion control in dynamics level through dynamics compensation using neural networks. Finally, the control scenario is extended from robot motion control to hybrid force and motion control, motivated by a robotic deep rolling task. We apply the model-free ILC algorithm to deep rolling on flat and curved surfaces.